Post-training

As explained in Reinforcement Learning from Human Feedback

Post-training is the set of optimization and adaptation procedures applied to a pretrained model to improve its usefulness for downstream tasks or alignment objectives.

Post-training is the many-stage optimization and adaptation process applied after pretraining to turn a next-token language model into a more useful model for downstream interactions. It cultivates capabilities already latent in a base model while reshaping outputs toward instruction following, preferred style, format, and task performance. section 1.1 section 1.4

Rather than one algorithm, post-training combines methods selected for different signals. Instruction fine-tuning (SFT or IFT) teaches the instruction–response format; preference fine-tuning aligns outputs with human preferences; and reinforcement learning with verifiable rewards (RLVR) targets domains whose correctness can be checked. RLHF is a major part of preference fine-tuning, not the entirety of post-training. section 1.1 section 1.7

A practical post-training pipeline can include:

• Instruction fine-tuning on curated instruction–response examples, with chat templates and loss masking that train assistant responses rather than user prompts. section 4.4
• Preference-data collection, ideally from generations of a recent checkpoint so comparisons reflect the model behaviors being optimized. section 11.2
• Preference optimization through a learned reward model and policy gradients, rejection sampling, or direct alignment algorithms trained from pairwise preferences. section 3.1.5
• RLVR runs on verifiable tasks such as mathematics, used to boost targeted skills while preserving broader performance. section 3.2.2

From completion to assistance

A raw model may continue a factual prompt with unrelated web-like metadata. A model shaped with RLHF and other post-training methods instead gives a concise answer. The same process can change interpersonal behavior: a blunt response to presentation anxiety can become a supportive response with concrete advice. These changes concern style, tone, and answer format, while post-training can also affect benchmark performance. section 1.2 section 1.7

Figure 3.7 A summary of the Tülu 3 recipe with target skills and multistep training recipe. Lambert et al. 2024, license CC-BY.

This multistage recipe connects large-scale instruction tuning, on-policy preference data, and a smaller RLVR stage to distinct target behaviors and skills. section 3.2.2

Recipes have expanded from the early SFT–reward-model–RL sequence into iterative pipelines with many checkpoints, larger synthetic datasets, and specialized training runs. Synthetic data can generate prompts and completions, supply AI feedback, or filter candidates; quality filters, diversity, deduplication, and mixing human data help avoid degraded generalization from repetitive self-training. section 3.2.1 section 12.1

Optimization choices carry trade-offs. Preference optimization uses imperfect proxy signals for human values, and over-optimization can produce excessive length or chattiness that harms performance elsewhere. Evaluation must also match the trained interaction format: prompt-format mismatches can sharply reduce scores, while differing prompts, sampling settings, and contamination make cross-system rankings approximate. section 1.7 section 16.6

How Reinforcement Learning from Human Feedback covers it

How deep it goes

Develops multistage recipes, data choices, optimization tools, and evaluation trade-offs rather than treating post-training as a single method.

Examples

Uses raw versus assistant-like answers, supportive chat responses, InstructGPT, and the Tülu 3 multistage recipe.

What it assumes

Assumes a pretrained next-token language model and introduces instruction–response formatting as the foundation for later stages.

What you can do after

Apply it via instruction tuning, preference optimization, and verifiable-reward training while assessing data and evaluation trade-offs.

How it connects

Connects instruction fine-tuning, RLHF, reward modeling, preference data, RLVR, synthetic data, and evaluation.

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Also explained in

• 

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  Introduction to Generative AI, Second EditionPost-training is the adaptation of a pretrained model using additional task-specific data and optimization rather than training the model from scratch.
• 

  liveBookmanning.com
  Build a Reasoning Model (From Scratch)Methods applied after initial pretraining to improve model behavior or capabilities.

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Related concepts

• fine-tuninghas part · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
• foundational modeluses · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
• reinforcement learninghas part · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
• RLAIFincludes · Reinforcement learning from AI feedback is a training or alignment technique that uses AI-generated evaluations or preferences as feedback to optimize a model or agent.
• Direct Preference Optimizationincludes · Direct Preference Optimization is a technique for training a model directly from preference data, such as preferred and dispreferred outputs, without separately fitting a reward model.
• reinforcement learning with human feedbackhas part · Reinforcement learning with human feedback is a model-training approach that uses human evaluations or preferences to guide optimization of model behavior.

This explainer was generated by Manning's knowledge graph from the text of Reinforcement Learning from Human Feedback. The section links point to the parts of the book it draws on.